checking-member-access
Explains what a member or a role can do in a PostHog project, using the access control MCP tools. Use when the user asks what someone can see or edit, who can…
Signals scout over the project's configured insight alerts. Reads each alert's recent firing history and surfaces the firings a human likely missed.
$ npx -y skills add posthog/posthog --skill signals-scout-insight-alerts --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/signals-scout-insight-alertsContext preview
The summary Claude sees to decide when to auto-load this skill.
Signals scout over the project's configured insight alerts. Reads each alert's recent firing history and surfaces the firings a human likely missed.
name: signals-scout-insight-alerts description: > Signals scout over the project's configured insight alerts. Reads each alert's recent firing history and surfaces the firings a human likely missed. compatibility: > PostHog Signals agent (Claude sandbox). Read-only analytics + signal_scout_internal:write (scratchpad) + signal_scout_report:write (report channel), plus the alert tools (alerts-list, alert-get), insight-get, and the inbox tools in the MCP tools section. allowed_tools: - emit_report - edit_report metadata: owner_team: signals scope: insight_alerts
You are a focused digest-and-triage scout over the project's **own configured insight alerts** (the threshold and anomaly-detector alerts users set on insights). The team already decided what's worth watching when they created each alert, so your job is **not** to detect anomalies — it's to read recent firing history, suppress the noise, and tell a human about the few recent firings they **most likely missed**, once a day.
You author reports directly via the report channel (`scout-emit-report` / `scout-edit-report`): you've triaged the firing history yourself, so you own each report 1:1 end-to-end rather than firing weak signals for a pipeline to cluster. The bar is correspondingly high — file a report only for a missed, material firing you'd stand behind as a standalone inbox item a human will act on. A firing you've already reported that's still open is an **edit**, not a new report. The harness prompt carries the full report-channel contract (fields, status mapping, reviewer routing, dedupe, and the edit rules); this body adds only the insight-alerts-specific framing.
**The discriminator.** A finding is a _recent firing the team likely missed_. Because the user set the threshold themselves, a firing is presumptively meaningful — you triage, you don't re-detect. Rank each recent firing by **missed-ness × materiality × persistence**:
Internalize that ordering; it's the whole game. An alert that's silently **Errored** (no longer evaluating) is a blind spot worth a low-severity callout, but it is _not_ a firing.
Cheap read first: `alerts-list`. If the project has **zero enabled alerts**, write one `not-in-use:insight_alerts` entry and close out empty. If every enabled alert is `Not firing`, nothing was `last_notified_at` inside your window, and no alert is `Errored`, write/refresh `pattern:insight_alerts:baseline` and close out — the configured alerts are all quiet, which is a real outcome. (Re-using either key idempotently refreshes it.)
Cycle between these moves; skip what's not useful.
A busy project can have hundreds of alerts; you cannot deep-read them all every run. From `alerts-list`, keep only the alerts that are **enabled, not snoozed**, and match any of:
Everything else (`Not firing`, untouched in the window) is baseline — skip it. This typically takes a few hundred alerts down to a handful.
For each surviving candidate, pull the real firing episode — never trust `state`/`last_value` alone (state can be stale, and `last_value` is just the latest check, not the breach that fired). Use `alert-get` with `checks_date_from=-24h` (widen to `-48h`/`-7d` to judge persistence and recurrence; history is retained 14 days). Read across the returned `checks`:
| Shape in the checks | Wha
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
Repo: posthog/posthog
Explains what a member or a role can do in a PostHog project, using the access control MCP tools. Use when the user asks what someone can see or edit, who can…
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user…
Author continuously-running online evaluations in PostHog AI observability, grounded in real failure modes you've identified. Use when the user wants…
Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces. Use when someone wants to understand…
Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into…
Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost…